Validity of 3-Tesla diffusion-weighted magnetic resonance imaging for distinction of reactive and metastatic lymph nodes in head-and-neck carcinoma
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Date
2020-07
Journal Title
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Publisher
Wolters Kluwer India Pvt. Ltd.
Abstract
Objectives: The objective was to study the relationship of 3-Tesla (3T) diffusion-weighted magnetic resonance imaging (DW-MRI) with apparent diffusion coefficient (ADC) value for distinction of reactive and metastatic lymph nodes (LNs) in head-and-neck carcinoma (HNC) patients and to determine the ADC cutoff value for metastatic LNs at various levels. Materials and Methods: 3T DW and T1- and T2-weighted imaging sequences were done in 34 patients with biopsy-proven primary HNC of 100 cervical LNs ≥1 cm in diameter. The mean ADC values were compared with histopathologically proven LNs using the independent t-test. ADC cutoff value was evaluated with sensitivity, specificity, accuracy, positive predictive value, negative predictive value and a receiver operating characteristic curve analysis. Results: The mean ADC value of reactive LN was 1.2933 × 10-3 mm2/s and metastatic LN was 0.908 × 10-3 mm2/s. An ADC cutoff value was 0.868 × 10-3 mm2/s with 84% sensitivity, 96% specificity, 93% accuracy, 87.5% positive predictive value, and 94.7% negative predictive value. A significant difference in mean ADC value between reactive and metastatic LNs was noted (P< 0.001). Conclusion: 3T DW-MRI is useful in differentiating reactive and metastatic cervical LNs in HNC patients. However, studies with larger sample size have to be performed to validate ADC threshold value with 3T DW-MRI in differentiating between reactive and metastatic LNs for clinical practice
Description
Keywords
Apparent diffusion coefficient values, cervical nodes, diffusion magnetic resonance imaging, head-and-neck neoplasms
Citation
K Vijayalakshmi, PH Raghuram, K Saravanan, CL Krithika, A Kannan. Validity of 3-Tesla diffusion-weighted magnetic resonance imaging for distinction of reactive and metastatic lymph nodes in head-and-neck carcinoma. Journal of Cancer Research and Therapeutics. 2020 Jul; 16(3): 587-593